Tal Arbel

Biography

Tal Arbel is a professor in the Department of Electrical and Computer Engineering at McGill University, where she is the director of the Probabilistic Vision Group and Medical Imaging Lab in the Centre for Intelligent Machines.

She is also a Canada CIFAR AI Chair, an associate academic member of Mila – Quebec Artificial Intelligence Institute and an associate member of the Goodman Cancer Research Centre.

Arbel’s research focuses on the development of probabilistic deep learning methods in computer vision and medical image analysis for a wide range of real-world applications, with a focus on neurological diseases.

She is a recipient of the 2019 McGill Engineering Christophe Pierre Research Award and a Fellow of the Canadian Academy of Engineering. She regularly serves on the organizing team of major international conferences in computer vision and in medical image analysis (e.g. MICCAI, MIDL, ICCV, CVPR). She is currently the Editor-in-Chief and co-founder of the arXiv overlay journal: Machine Learning for Biomedical Imaging (MELBA).

Current Students

Cornelius Crijnen

Collaborating researcher - Université de Montréal

Nima Fathi

Master's Research - McGill University

Gian Favero

Master's Research - McGill University

Elizabeth Laura Janes

Master's Research - McGill University

Emily Kaczmarek

PhD - McGill University

Amar Kumar

PhD - McGill University

Mohamed Mohamed

Master's Research - McGill University

Toky Raharison Ralambomihanta

Undergraduate - McGill University

Ryan Rezai

Master's Research - McGill University

Parham Saremi

Master's Research - McGill University

PRISM: An Explainable Generative AI Model for Medical Imaging

Xing Shen

Master's Research - McGill University

Master's Research - McGill University

Blog Posts

Image of an Xray and the DDIM process to generate counterfactual version of Xrays

July 1, 2025

Amar Kumar

Anita Kriz

Mohammed Havaei

Tal Arbel

Read the article

Publications

On Learning Fairness and Accuracy on Multiple Subgroups

Changjian Shui

Gezheng Xu

Qi CHEN

Jiaqi Li

Charles Ling

Boyu Wang

Christian Gagné

We propose an analysis in fair learning that preserves the utility of the data while reducing prediction disparities under the criteria of g… (see more)roup sufficiency. We focus on the scenario where the data contains multiple or even many subgroups, each with limited number of samples. As a result, we present a principled method for learning a fair predictor for all subgroups via formulating it as a bilevel objective. Specifically, the subgroup specific predictors are learned in the lower-level through a small amount of data and the fair predictor. In the upper-level, the fair predictor is updated to be close to all subgroup specific predictors. We further prove that such a bilevel objective can effectively control the group sufficiency and generalization error. We evaluate the proposed framework on real-world datasets. Empirical evidence suggests the consistently improved fair predictions, as well as the comparable accuracy to the baselines.

Estimating treatment effect for individuals with progressive multiple sclerosis using deep learning

JR Falet

Joshua D. Durso-Finley

Jan Schroeter

Francesca Bovis

Maria-Pia Sormani

Doina Precup

Douglas Arnold

2021-11-01

medRxiv (preprint)

doi.org

Cohort Bias Adaptation in Aggregated Datasets for Lesion Segmentation

Jillian L. Cardinell

Justin Szeto

Raghav Mehta

Sotirios A. Tsaftaris

Douglas Arnold

Nazanin Mohammadi Sepahvand

2021-09-21

Domain Adaptation and Representation Transfer, and Affordable Healthcare and AI for Resource Diverse Global Health (published)

doi.org

arxiv.org

HAD-Net: A Hierarchical Adversarial Knowledge Distillation Network for Improved Enhanced Tumour Segmentation Without Post-Contrast Images

Saverio Vadacchino

Raghav Mehta

James J. Clark

Segmentation of enhancing tumours or lesions from MRI is important for detecting new disease activity in many clinical contexts. However, ac… (see more)curate segmentation requires the inclusion of medical images (e.g., T1 post-contrast MRI) acquired after injecting patients with a contrast agent (e.g., Gadolinium), a process no longer thought to be safe. Although a number of modality-agnostic segmentation networks have been developed over the past few years, they have been met with limited success in the context of enhancing pathology segmentation. In this work, we present HAD-Net, a novel offline adversarial knowledge distillation (KD) technique, whereby a pre-trained teacher segmentation network, with access to all MRI sequences, teaches a student network, via hierarchical adversarial training, to better overcome the large domain shift presented when crucial images are absent during inference. In particular, we apply HAD-Net to the challenging task of enhancing tumour segmentation when access to post-contrast imaging is not available. The proposed network is trained and tested on the BraTS 2019 brain tumour segmentation challenge dataset, where it achieves performance improvements in the ranges of 16% - 26% over (a) recent modality-agnostic segmentation methods (U-HeMIS, U-HVED), (b) KD-Net adapted to this problem, (c) the pre-trained student network and (d) a non-hierarchical version of the network (AD-Net), in terms of Dice scores for enhancing tumour (ET). The network also shows improvements in tumour core (TC) Dice scores. Finally, the network outperforms both the baseline student network and AD-Net in terms of uncertainty quantification for enhancing tumour segmentation based on the BraTS 2019 uncertainty challenge metrics. Our code is publicly available at: https://github.com/SaverioVad/HAD_Net

2021-08-25

Proceedings of the Fourth Conference on Medical Imaging with Deep Learning (published)

proceedings.mlr.press

Common limitations of performance metrics in biomedical image analysis

Annika Reinke

Matthias Eisenmann

Minu Dietlinde Tizabi

Carole H. Sudre

TIM RÄDSCH

Michela Antonelli

Spyridon Bakas

M. Jorge Cardoso

Veronika Cheplygina

Keyvan Farahani

Ben Glocker

DOREEN HECKMANN-NÖTZEL

Fabian Isensee

Pierre Jannin

Charles Kahn

Jens Kleesiek

Tahsin Kurc

Michal Kozubek

Bennett Landman … (see 15 more)

GEERT LITJENS

Klaus Maier-Hein

Anne Martel

Bjoern Menze

Henning Müller

Jens Petersen

Mauricio Reyes

Nicola Rieke

Bram Stieltjes

Ronald M. Summers

Sotirios A. Tsaftaris

Bram van Ginneken

Annette Kopp-Schneider

Paul Jäger

Lena Maier-Hein

2021-05-11

MIDL.io/2021/Conference/Short (poster)

Optimizing Operating Points for High Performance Lesion Detection and Segmentation Using Lesion Size Reweighting

Justin Szeto

Douglas Arnold

There are many clinical contexts which require accurate detection and segmentation of all focal pathologies (e.g. lesions, tumours) in patie… (see more)nt images. In cases where there are a mix of small and large lesions, standard binary cross entropy loss will result in better segmentation of large lesions at the expense of missing small ones. Adjusting the operating point to accurately detect all lesions generally leads to oversegmentation of large lesions. In this work, we propose a novel reweighing strategy to eliminate this performance gap, increasing small pathology detection performance while maintaining segmentation accuracy. We show that our reweighing strategy vastly outperforms competing strategies based on experiments on a large scale, multi-scanner, multi-center dataset of Multiple Sclerosis patient images.

2021-05-11

MIDL.io/2021/Conference/Short (poster)

Common Limitations of Image Processing Metrics: A Picture Story

Annika Reinke

Matthias Eisenmann

Minu Dietlinde Tizabi

Carole H. Sudre

TIM RÄDSCH

Michela Antonelli

Spyridon Bakas

M. Jorge Cardoso

Veronika Cheplygina

Keyvan Farahani

B. Glocker

DOREEN HECKMANN-NÖTZEL

Fabian Isensee

Pierre Jannin

Charles E. Jr. Kahn

Jens Kleesiek

Tahsin Kurc

Michal Kozubek

Bennett Landman … (see 14 more)

GEERT LITJENS

Klaus Maier-Hein

Bjoern Menze

Henning Müller

Jens Petersen

Mauricio Reyes

Nicola Rieke

Bram Stieltjes

R. Summers

Sotirios A. Tsaftaris

Bram van Ginneken

Annette Kopp-Schneider

PAUL F. JÄGER

Lena Maier-Hein

2021-04-12

ArXiv (preprint)

arxiv.org

Task dependent deep LDA pruning of neural networks

Qing Tian

James J. Clark

2021-02-01

Computer Vision and Image Understanding (published)

doi.org

Accounting for Variance in Machine Learning Benchmarks

Assya Trofimov

Naz Sepah

Edward Raff

Kanika Madan

Vikram Voleti

Samira Ebrahimi Kahou

Dmitriy Serdyuk

Gael Varoquaux

Strong empirical evidence that one machine-learning algorithm A outperforms another one B ideally calls for multiple trials optimizing the l… (see more)earning pipeline over sources of variation such as data sampling, data augmentation, parameter initialization, and hyperparameters choices. This is prohibitively expensive, and corners are cut to reach conclusions. We model the whole benchmarking process, revealing that variance due to data sampling, parameter initialization and hyperparameter choice impact markedly the results. We analyze the predominant comparison methods used today in the light of this variance. We show a counter-intuitive result that adding more sources of variation to an imperfect estimator approaches better the ideal estimator at a 51 times reduction in compute cost. Building on these results, we study the error rate of detecting improvements, on five different deep-learning tasks/architectures. This study leads us to propose recommendations for performance comparisons.

2021-01-01

MLSys (published)

arxiv.org

Deep LDA-Pruned Nets for Efﬁcient Facial Gender Classiﬁcation

Qing Tian

James J. Clark

Many real-time tasks, such as human-computer interac-tion, require fast and efﬁcient facial gender classiﬁcation. Although deep CNN nets… (see more) have been very effective for a mul-titude of classiﬁcation tasks, their high space and time de-mands make them impractical for personal computers and mobile devices without a powerful GPU. In this paper, we develop a 16-layer, yet lightweight, neural network which boosts efﬁciency while maintaining high accuracy. Our net is pruned from the VGG-16 model [35] starting from the last convolutional (conv) layer where we ﬁnd neuron activations are highly uncorrelated given the gender. Through Fisher’s Linear Discriminant Analysis (LDA) [8], we show that this high decorrelation makes it safe to discard directly last conv layer neurons with high within-class variance and low between-class variance. Combined with either Support Vector Machines (SVM) or Bayesian classiﬁcation, the reduced CNNs are capable of achieving comparable (or even higher) accuracies on the LFW and CelebA datasets than the original net with fully connected layers. On LFW, only four Conv5 3 neurons are able to maintain a comparably high recognition accuracy, which results in a reduction of total network size by a factor of 70X with a 11 fold speedup. Comparisons with a state-of-the-art pruning method [12] (as well as two smaller nets [20, 24]) in terms of accuracy loss and convolutional layers pruning rate are also provided.

2021-01-01

(published)

www.semanticscholar.org

Deep discriminant analysis for task-dependent compact network search

Qing Tian

James J. Clark

Most of today's popular deep architectures are hand-engineered for general purpose applications. However, this design procedure usually lead… (see more)s to massive redundant, useless, or even harmful features for specific tasks. Such unnecessarily high complexities render deep nets impractical for many real-world applications, especially those without powerful GPU support. In this paper, we attempt to derive task-dependent compact models from a deep discriminant analysis perspective. We propose an iterative and proactive approach for classification tasks which alternates between (1) a pushing step, with an objective to simultaneously maximize class separation, penalize co-variances, and push deep discriminants into alignment with a compact set of neurons, and (2) a pruning step, which discards less useful or even interfering neurons. Deconvolution is adopted to reverse `unimportant' filters' effects and recover useful contributing sources. A simple network growing strategy based on the basic Inception module is proposed for challenging tasks requiring larger capacity than what the base net can offer. Experiments on the MNIST, CIFAR10, and ImageNet datasets demonstrate our approach's efficacy. On ImageNet, by pushing and pruning our grown Inception-88 model, we achieve better-performing models than smaller deep Inception nets grown, residual nets, and famous compact nets at similar sizes. We also show that our grown deep Inception nets (without hard-coded dimension alignment) can beat residual nets of similar complexities.

2020-09-29

arXiv.org (preprint)

dblp.uni-trier.de

Preface